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#pragma once |
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#include <gtest/gtest.h> |
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#include <algorithm> |
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#include <cassert> |
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#include <cmath> |
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#include <cstddef> |
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#include <cstdlib> |
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#include <limits> |
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#include <random> |
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#include <vector> |
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#include <fp16/fp16.h> |
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#include <xnnpack.h> |
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#include <xnnpack/aligned-allocator.h> |
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#include <xnnpack/microfnptr.h> |
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#include <xnnpack/microparams-init.h> |
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#include <xnnpack/requantization.h> |
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class AvgPoolMicrokernelTester { |
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public: |
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inline AvgPoolMicrokernelTester& output_pixels(size_t output_pixels) { |
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assert(output_pixels != 0); |
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this->output_pixels_ = output_pixels; |
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return *this; |
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} |
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inline size_t output_pixels() const { |
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return this->output_pixels_; |
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} |
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inline AvgPoolMicrokernelTester& step(size_t step) { |
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assert(step != 0); |
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this->step_ = step; |
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return *this; |
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} |
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inline size_t step() const { |
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return this->step_; |
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} |
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inline AvgPoolMicrokernelTester& input_offset(size_t input_offset) { |
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assert(input_offset != 0); |
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this->input_offset_ = input_offset; |
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return *this; |
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} |
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inline size_t input_offset() const { |
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return this->input_offset_; |
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} |
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inline AvgPoolMicrokernelTester& zero_index(size_t zero_index) { |
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this->zero_index_ = zero_index; |
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return *this; |
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} |
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inline size_t zero_index() const { |
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return this->zero_index_; |
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} |
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inline AvgPoolMicrokernelTester& pooling_elements(size_t pooling_elements) { |
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assert(pooling_elements != 0); |
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this->pooling_elements_ = pooling_elements; |
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return *this; |
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} |
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inline size_t pooling_elements() const { |
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return this->pooling_elements_; |
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} |
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inline size_t packed_pooling_elements() const { |
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if (pooling_elements() <= primary_pooling_tile()) { |
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return primary_pooling_tile(); |
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} else { |
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return (pooling_elements() - primary_pooling_tile()) % incremental_pooling_tile() == 0 ? pooling_elements() : ((pooling_elements() - primary_pooling_tile()) / incremental_pooling_tile() + 1) * incremental_pooling_tile() + primary_pooling_tile(); |
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} |
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} |
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inline AvgPoolMicrokernelTester& pooling_tile(size_t primary_tile, size_t incremental_tile = 0) { |
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assert(primary_tile != 0); |
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this->primary_pooling_tile_ = primary_tile; |
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this->incremental_pooling_tile_ = incremental_tile; |
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return *this; |
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} |
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inline AvgPoolMicrokernelTester& primary_pooling_tile(size_t primary_pooling_tile) { |
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assert(primary_pooling_tile != 0); |
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this->primary_pooling_tile_ = primary_pooling_tile; |
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return *this; |
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} |
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inline size_t primary_pooling_tile() const { |
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return this->primary_pooling_tile_; |
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} |
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inline AvgPoolMicrokernelTester& incremental_pooling_tile(size_t incremental_pooling_tile) { |
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assert(incremental_pooling_tile != 0); |
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this->incremental_pooling_tile_ = incremental_pooling_tile; |
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return *this; |
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} |
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inline size_t incremental_pooling_tile() const { |
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return this->incremental_pooling_tile_; |
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} |
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inline AvgPoolMicrokernelTester& channels(size_t channels) { |
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assert(channels != 0); |
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this->channels_ = channels; |
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return *this; |
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} |
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inline size_t channels() const { |
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return this->channels_; |
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} |
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inline AvgPoolMicrokernelTester& output_stride(size_t output_stride) { |
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assert(output_stride != 0); |
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this->output_stride_ = output_stride; |
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return *this; |
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} |
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inline size_t output_stride() const { |
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if (this->output_stride_ == 0) { |
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return channels(); |
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} else { |
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assert(this->output_stride_ >= channels()); |
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return this->output_stride_; |
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} |
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} |
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inline AvgPoolMicrokernelTester& input_scale(float input_scale) { |
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assert(input_scale > 0.0f); |
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assert(std::isnormal(input_scale)); |
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this->input_scale_ = input_scale; |
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return *this; |
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} |
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inline float input_scale() const { |
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return this->input_scale_; |
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} |
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inline AvgPoolMicrokernelTester& input_zero_point(uint8_t input_zero_point) { |
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this->input_zero_point_ = input_zero_point; |
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return *this; |
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} |
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inline uint8_t input_zero_point() const { |
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return this->input_zero_point_; |
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} |
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inline AvgPoolMicrokernelTester& output_scale(float output_scale) { |
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assert(output_scale > 0.0f); |
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assert(std::isnormal(output_scale)); |
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this->output_scale_ = output_scale; |
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return *this; |
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} |
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inline float output_scale() const { |
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return this->output_scale_; |
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} |
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inline AvgPoolMicrokernelTester& output_zero_point(uint8_t output_zero_point) { |
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this->output_zero_point_ = output_zero_point; |
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return *this; |
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} |
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inline uint8_t output_zero_point() const { |
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return this->output_zero_point_; |
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} |
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inline AvgPoolMicrokernelTester& qmin(uint8_t qmin) { |
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this->qmin_ = qmin; |
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return *this; |
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} |
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inline uint8_t qmin() const { |
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return this->qmin_; |
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} |
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inline AvgPoolMicrokernelTester& qmax(uint8_t qmax) { |
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this->qmax_ = qmax; |
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return *this; |
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} |
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inline uint8_t qmax() const { |
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return this->qmax_; |
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} |
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inline AvgPoolMicrokernelTester& iterations(size_t iterations) { |
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this->iterations_ = iterations; |
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return *this; |
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} |
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inline size_t iterations() const { |
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return this->iterations_; |
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} |
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void Test(xnn_f16_avgpool_minmax_unipass_ukernel_fn avgpool_minmax, xnn_init_f16_scaleminmax_params_fn init_params) const { |
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std::random_device random_device; |
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auto rng = std::mt19937(random_device()); |
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std::uniform_real_distribution<float> f32dist; |
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std::vector<const uint16_t*> indirect_input((output_pixels() - 1) * step() + packed_pooling_elements()); |
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std::vector<uint16_t> input(XNN_EXTRA_BYTES / sizeof(uint16_t) + |
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input_offset() + indirect_input.size() * channels()); |
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std::vector<uint16_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(uint16_t)); |
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std::vector<uint16_t> output((output_pixels() - 1) * output_stride() + channels()); |
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std::vector<float> output_ref(output_pixels() * channels()); |
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for (size_t iteration = 0; iteration < iterations(); iteration++) { |
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std::generate(input.begin(), input.end(), [&]() { return fp16_ieee_from_fp32_value(f32dist(rng)); }); |
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std::fill(input.begin(), input.begin() + input_offset(), UINT16_C(0x7E00) ); |
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std::fill(input.end() - XNN_EXTRA_BYTES / sizeof(uint16_t), input.end(), UINT16_C(0x7E00) ); |
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std::fill(output.begin(), output.end(), UINT16_C(0x7E00) ); |
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for (size_t i = 0; i < (output_pixels() - 1) * step() + pooling_elements(); i++) { |
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indirect_input[i] = input.data() + i * channels(); |
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} |
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std::shuffle(indirect_input.begin(), |
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indirect_input.begin() + (output_pixels() - 1) * step() + pooling_elements(), rng); |
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if (zero_index() != SIZE_MAX) { |
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indirect_input[zero_index()] = zero.data(); |
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} |
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for (size_t x = 0; x < output_pixels(); x++) { |
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for (size_t c = 0; c < channels(); c++) { |
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float acc = 0.0f; |
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for (size_t p = 0; p < pooling_elements(); p++) { |
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const uint16_t* row = indirect_input[x * step() + p]; |
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if (row != zero.data()) { |
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acc += fp16_ieee_to_fp32_value(row[c + input_offset()]); |
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} |
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} |
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output_ref[x * channels() + c] = acc / float(pooling_elements()); |
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} |
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} |
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const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend()); |
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const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend()); |
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const float accumulated_range = accumulated_max - accumulated_min; |
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float output_min_as_float = accumulated_min + float(qmin()) / 255.0f * accumulated_range; |
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float output_max_as_float = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range; |
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const uint16_t output_min_as_half = fp16_ieee_from_fp32_value(output_min_as_float); |
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const uint16_t output_max_as_half = fp16_ieee_from_fp32_value(output_max_as_float); |
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output_min_as_float = fp16_ieee_to_fp32_value(output_min_as_half); |
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output_max_as_float = fp16_ieee_to_fp32_value(output_max_as_half); |
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for (float& output_value : output_ref) { |
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output_value = std::max(std::min(output_value, output_max_as_float), output_min_as_float); |
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} |
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xnn_f16_scaleminmax_params params; |
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init_params(¶ms, fp16_ieee_from_fp32_value(1.0f / float(pooling_elements())), output_min_as_half, output_max_as_half); |
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avgpool_minmax(output_pixels(), pooling_elements(), channels(), |
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reinterpret_cast<const void**>(indirect_input.data()), input_offset() * sizeof(uint16_t), zero.data(), |
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output.data(), |
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step() * sizeof(void*), |
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(output_stride() - channels()) * sizeof(uint16_t), |
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¶ms); |
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for (size_t x = 0; x < output_pixels(); x++) { |
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for (size_t c = 0; c < channels(); c++) { |
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EXPECT_GE(fp16_ieee_to_fp32_value(output[x * output_stride() + c]), output_min_as_float) |
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
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<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
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<< ", input offset = " << input_offset(); |
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EXPECT_LE(fp16_ieee_to_fp32_value(output[x * output_stride() + c]), output_max_as_float) |
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
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<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
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<< ", input offset = " << input_offset(); |
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EXPECT_NEAR( |
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fp16_ieee_to_fp32_value(output[x * output_stride() + c]), |
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output_ref[x * channels() + c], |
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std::max(1.0e-4f, std::abs(output_ref[x * channels() + c]) * 3.0e-3f)) |
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
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<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
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<< ", input offset = " << input_offset(); |
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} |
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} |
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} |
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} |
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void Test(xnn_f16_avgpool_minmax_multipass_ukernel_fn avgpool_minmax, xnn_init_f16_scaleminmax_params_fn init_params) const { |
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std::random_device random_device; |
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auto rng = std::mt19937(random_device()); |
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std::uniform_real_distribution<float> f32dist; |
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std::vector<const uint16_t*> indirect_input((output_pixels() - 1) * step() + packed_pooling_elements()); |
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std::vector<uint16_t> input(XNN_EXTRA_BYTES / sizeof(uint16_t) + |
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input_offset() + indirect_input.size() * channels()); |
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std::vector<uint16_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(uint16_t)); |
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std::vector<uint16_t> output((output_pixels() - 1) * output_stride() + channels()); |
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std::vector<float> output_ref(output_pixels() * channels()); |
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std::vector<uint16_t, AlignedAllocator<uint16_t, 64>> buffer(XNN_EXTRA_BYTES / sizeof(uint16_t) + channels()); |
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for (size_t iteration = 0; iteration < iterations(); iteration++) { |
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std::generate(input.begin(), input.end(), [&]() { return fp16_ieee_from_fp32_value(f32dist(rng)); }); |
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std::fill(input.begin(), input.begin() + input_offset(), UINT16_C(0x7E00) ); |
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std::fill(input.end() - XNN_EXTRA_BYTES / sizeof(uint16_t), input.end(), UINT16_C(0x7E00) ); |
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std::fill(output.begin(), output.end(), UINT16_C(0x7E00) ); |
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for (size_t i = 0; i < (output_pixels() - 1) * step() + pooling_elements(); i++) { |
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indirect_input[i] = input.data() + i * channels(); |
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} |
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std::shuffle(indirect_input.begin(), |
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indirect_input.begin() + (output_pixels() - 1) * step() + pooling_elements(), rng); |
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if (zero_index() != SIZE_MAX) { |
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indirect_input[zero_index()] = zero.data(); |
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} |
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for (size_t x = 0; x < output_pixels(); x++) { |
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for (size_t c = 0; c < channels(); c++) { |
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float acc = 0.0f; |
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for (size_t p = 0; p < pooling_elements(); p++) { |
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const uint16_t* row = indirect_input[x * step() + p]; |
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if (row != zero.data()) { |
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acc += fp16_ieee_to_fp32_value(row[c + input_offset()]); |
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} |
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} |
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output_ref[x * channels() + c] = acc / float(pooling_elements()); |
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} |
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} |
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const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend()); |
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const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend()); |
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const float accumulated_range = accumulated_max - accumulated_min; |
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float output_min_as_float = accumulated_min + float(qmin()) / 255.0f * accumulated_range; |
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float output_max_as_float = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range; |
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const uint16_t output_min_as_half = fp16_ieee_from_fp32_value(output_min_as_float); |
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const uint16_t output_max_as_half = fp16_ieee_from_fp32_value(output_max_as_float); |
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output_min_as_float = fp16_ieee_to_fp32_value(output_min_as_half); |
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output_max_as_float = fp16_ieee_to_fp32_value(output_max_as_half); |
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for (float& output_value : output_ref) { |
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output_value = std::max(std::min(output_value, output_max_as_float), output_min_as_float); |
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} |
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xnn_f16_scaleminmax_params params; |
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init_params(¶ms, fp16_ieee_from_fp32_value(1.0f / float(pooling_elements())), output_min_as_half, output_max_as_half); |
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avgpool_minmax(output_pixels(), pooling_elements(), channels(), |
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reinterpret_cast<const void**>(indirect_input.data()), input_offset() * sizeof(uint16_t), zero.data(), |
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buffer.data(), output.data(), |
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(step() - (packed_pooling_elements() - incremental_pooling_tile())) * sizeof(void*), |
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(output_stride() - channels()) * sizeof(uint16_t), |
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¶ms); |
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for (size_t x = 0; x < output_pixels(); x++) { |
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for (size_t c = 0; c < channels(); c++) { |
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EXPECT_GE(fp16_ieee_to_fp32_value(output[x * output_stride() + c]), output_min_as_float) |
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
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<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
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<< ", input offset = " << input_offset(); |
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EXPECT_LE(fp16_ieee_to_fp32_value(output[x * output_stride() + c]), output_max_as_float) |
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
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<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
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<< ", input offset = " << input_offset(); |
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EXPECT_NEAR( |
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fp16_ieee_to_fp32_value(output[x * output_stride() + c]), |
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output_ref[x * channels() + c], |
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std::max(1.0e-4f, std::abs(output_ref[x * channels() + c]) * 3.0e-3f)) |
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
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<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
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<< ", input offset = " << input_offset(); |
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} |
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} |
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} |
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} |
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void Test(xnn_f32_avgpool_minmax_unipass_ukernel_fn avgpool_minmax, xnn_init_f32_scaleminmax_params_fn init_params) const { |
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std::random_device random_device; |
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auto rng = std::mt19937(random_device()); |
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std::uniform_real_distribution<float> f32dist; |
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std::vector<const float*> indirect_input((output_pixels() - 1) * step() + packed_pooling_elements()); |
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std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + |
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input_offset() + indirect_input.size() * channels()); |
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std::vector<float> zero(channels() + XNN_EXTRA_BYTES / sizeof(float)); |
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std::vector<float> output((output_pixels() - 1) * output_stride() + channels()); |
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std::vector<float> output_ref(output_pixels() * channels()); |
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for (size_t iteration = 0; iteration < iterations(); iteration++) { |
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std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); }); |
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std::fill(input.begin(), input.begin() + input_offset(), std::nanf("")); |
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std::fill(input.end() - XNN_EXTRA_BYTES / sizeof(float), input.end(), std::nanf("")); |
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std::fill(output.begin(), output.end(), std::nanf("")); |
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for (size_t i = 0; i < (output_pixels() - 1) * step() + pooling_elements(); i++) { |
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indirect_input[i] = input.data() + i * channels(); |
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} |
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std::shuffle(indirect_input.begin(), |
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indirect_input.begin() + (output_pixels() - 1) * step() + pooling_elements(), rng); |
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if (zero_index() != SIZE_MAX) { |
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indirect_input[zero_index()] = zero.data(); |
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} |
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for (size_t x = 0; x < output_pixels(); x++) { |
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for (size_t c = 0; c < channels(); c++) { |
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float acc = 0.0f; |
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for (size_t p = 0; p < pooling_elements(); p++) { |
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const float* row = indirect_input[x * step() + p]; |
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if (row != zero.data()) { |
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acc += row[c + input_offset()]; |
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} |
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} |
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output_ref[x * channels() + c] = acc / float(pooling_elements()); |
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} |
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} |
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const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend()); |
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const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend()); |
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const float accumulated_range = accumulated_max - accumulated_min; |
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const float output_min = accumulated_min + float(qmin()) / 255.0f * accumulated_range; |
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const float output_max = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range; |
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for (float& output_value : output_ref) { |
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output_value = std::max(std::min(output_value, output_max), output_min); |
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} |
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xnn_f32_scaleminmax_params params; |
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init_params(¶ms, 1.0f / float(pooling_elements()), output_min, output_max); |
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avgpool_minmax(output_pixels(), pooling_elements(), channels(), |
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indirect_input.data(), input_offset() * sizeof(float), zero.data(), |
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output.data(), |
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step() * sizeof(void*), |
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(output_stride() - channels()) * sizeof(float), |
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¶ms); |
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for (size_t x = 0; x < output_pixels(); x++) { |
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for (size_t c = 0; c < channels(); c++) { |
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EXPECT_GE(output[x * output_stride() + c], output_min) |
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
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<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
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<< ", input offset = " << input_offset(); |
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EXPECT_LE(output[x * output_stride() + c], output_max) |
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
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<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
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<< ", input offset = " << input_offset(); |
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EXPECT_NEAR( |
|
output[x * output_stride() + c], |
|
output_ref[x * channels() + c], |
|
std::abs(output_ref[x * channels() + c]) * 1.0e-6f) |
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
|
<< ", input offset = " << input_offset(); |
|
} |
|
} |
|
} |
|
} |
|
|
|
void Test(xnn_f32_avgpool_minmax_multipass_ukernel_fn avgpool_minmax, xnn_init_f32_scaleminmax_params_fn init_params) const { |
|
std::random_device random_device; |
|
auto rng = std::mt19937(random_device()); |
|
std::uniform_real_distribution<float> f32dist; |
|
|
|
std::vector<const float*> indirect_input((output_pixels() - 1) * step() + packed_pooling_elements()); |
|
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + |
|
input_offset() + indirect_input.size() * channels()); |
|
std::vector<float> zero(channels() + XNN_EXTRA_BYTES / sizeof(float)); |
|
std::vector<float> output((output_pixels() - 1) * output_stride() + channels()); |
|
std::vector<float> output_ref(output_pixels() * channels()); |
|
std::vector<float, AlignedAllocator<float, 64>> buffer(XNN_EXTRA_BYTES / sizeof(float) + channels()); |
|
for (size_t iteration = 0; iteration < iterations(); iteration++) { |
|
std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); }); |
|
std::fill(input.begin(), input.begin() + input_offset(), std::nanf("")); |
|
std::fill(input.end() - XNN_EXTRA_BYTES / sizeof(float), input.end(), std::nanf("")); |
|
std::fill(output.begin(), output.end(), std::nanf("")); |
|
|
|
for (size_t i = 0; i < (output_pixels() - 1) * step() + pooling_elements(); i++) { |
|
indirect_input[i] = input.data() + i * channels(); |
|
} |
|
std::shuffle(indirect_input.begin(), |
|
indirect_input.begin() + (output_pixels() - 1) * step() + pooling_elements(), rng); |
|
if (zero_index() != SIZE_MAX) { |
|
indirect_input[zero_index()] = zero.data(); |
|
} |
|
|
|
|
|
for (size_t x = 0; x < output_pixels(); x++) { |
|
for (size_t c = 0; c < channels(); c++) { |
|
float acc = 0.0f; |
|
for (size_t p = 0; p < pooling_elements(); p++) { |
|
const float* row = indirect_input[x * step() + p]; |
|
if (row != zero.data()) { |
|
acc += row[c + input_offset()]; |
|
} |
|
} |
|
output_ref[x * channels() + c] = acc / float(pooling_elements()); |
|
} |
|
} |
|
|
|
|
|
const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend()); |
|
const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend()); |
|
const float accumulated_range = accumulated_max - accumulated_min; |
|
const float output_min = accumulated_min + float(qmin()) / 255.0f * accumulated_range; |
|
const float output_max = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range; |
|
|
|
|
|
for (float& output_value : output_ref) { |
|
output_value = std::max(std::min(output_value, output_max), output_min); |
|
} |
|
|
|
|
|
xnn_f32_scaleminmax_params params; |
|
init_params(¶ms, 1.0f / float(pooling_elements()), output_min, output_max); |
|
|
|
|
|
avgpool_minmax(output_pixels(), pooling_elements(), channels(), |
|
indirect_input.data(), input_offset() * sizeof(float), zero.data(), |
|
buffer.data(), output.data(), |
|
(step() - (packed_pooling_elements() - incremental_pooling_tile())) * sizeof(void*), |
|
(output_stride() - channels()) * sizeof(float), |
|
¶ms); |
|
|
|
|
|
for (size_t x = 0; x < output_pixels(); x++) { |
|
for (size_t c = 0; c < channels(); c++) { |
|
EXPECT_GE(output[x * output_stride() + c], output_min) |
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
|
<< ", input offset = " << input_offset(); |
|
EXPECT_LE(output[x * output_stride() + c], output_max) |
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
|
<< ", input offset = " << input_offset(); |
|
EXPECT_NEAR( |
|
output[x * output_stride() + c], |
|
output_ref[x * channels() + c], |
|
std::abs(output_ref[x * channels() + c]) * 1.0e-6f) |
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
|
<< ", input offset = " << input_offset(); |
|
} |
|
} |
|
} |
|
} |
|
|
|
void Test( |
|
xnn_qu8_avgpool_minmax_unipass_ukernel_fn avgpool_minmax, |
|
xnn_init_qu8_avgpool_minmax_params_fn init_params, |
|
xnn_qu8_requantize_fn requantize) const |
|
{ |
|
std::random_device random_device; |
|
auto rng = std::mt19937(random_device()); |
|
std::uniform_int_distribution<int32_t> u8dist( |
|
std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max()); |
|
|
|
std::vector<const uint8_t*> indirect_input((output_pixels() - 1) * step() + packed_pooling_elements()); |
|
std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + |
|
input_offset() + indirect_input.size() * channels()); |
|
std::vector<uint8_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(uint8_t)); |
|
std::vector<uint8_t> output((output_pixels() - 1) * output_stride() + channels()); |
|
std::vector<uint8_t> output_ref(output_pixels() * channels()); |
|
std::vector<float> output_real(output_pixels() * channels()); |
|
std::vector<int32_t> accumulator(output_pixels() * channels()); |
|
for (size_t iteration = 0; iteration < iterations(); iteration++) { |
|
do { |
|
std::generate(input.begin(), input.end(), [&]() { return u8dist(rng); }); |
|
} while (input.size() > 1 && *std::max_element(input.cbegin(), input.cend()) == *std::min_element(input.cbegin(), input.cend())); |
|
std::fill(input.begin(), input.begin() + input_offset(), UINT8_C(0xA5)); |
|
std::fill(input.end() - XNN_EXTRA_BYTES / sizeof(uint8_t), input.end(), UINT8_C(0xA5)); |
|
std::fill(output.begin(), output.end(), UINT8_C(0xA5)); |
|
|
|
for (size_t i = 0; i < (output_pixels() - 1) * step() + pooling_elements(); i++) { |
|
indirect_input[i] = input.data() + i * channels(); |
|
} |
|
std::shuffle(indirect_input.begin(), |
|
indirect_input.begin() + (output_pixels() - 1) * step() + pooling_elements(), rng); |
|
if (zero_index() != SIZE_MAX) { |
|
indirect_input[zero_index()] = zero.data(); |
|
} |
|
|
|
|
|
xnn_qu8_avgpool_minmax_params params; |
|
init_params( |
|
¶ms, |
|
-int32_t(input_zero_point()) * int32_t(pooling_elements()), |
|
input_scale() / (output_scale() * float(pooling_elements())), |
|
output_zero_point(), qmin(), qmax()); |
|
|
|
|
|
for (size_t x = 0; x < output_pixels(); x++) { |
|
for (size_t c = 0; c < channels(); c++) { |
|
int32_t acc = 0; |
|
for (size_t p = 0; p < pooling_elements(); p++) { |
|
const uint8_t* row = indirect_input[x * step() + p]; |
|
if (row != zero.data()) { |
|
acc += int32_t(row[c + input_offset()]); |
|
} |
|
acc -= int32_t(input_zero_point()); |
|
} |
|
accumulator[x * channels() + c] = acc; |
|
output_ref[x * channels() + c] = requantize( |
|
acc, input_scale() / (output_scale() * float(pooling_elements())), output_zero_point(), qmin(), qmax()); |
|
const float scaled_acc = |
|
float(acc) * input_scale() / (output_scale() * float(pooling_elements())) + float(output_zero_point()); |
|
output_real[x * channels() + c] = std::min(std::max(scaled_acc, float(qmin())), float(qmax())); |
|
} |
|
} |
|
|
|
|
|
avgpool_minmax(output_pixels(), pooling_elements(), channels(), |
|
indirect_input.data(), input_offset() * sizeof(uint8_t), zero.data(), |
|
output.data(), |
|
step() * sizeof(void*), |
|
(output_stride() - channels()) * sizeof(uint8_t), |
|
¶ms); |
|
|
|
|
|
for (size_t x = 0; x < output_pixels(); x++) { |
|
for (size_t c = 0; c < channels(); c++) { |
|
EXPECT_GE(uint32_t(output[x * output_stride() + c]), uint32_t(qmin())) |
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
|
<< ", input offset = " << input_offset(); |
|
EXPECT_LE(uint32_t(output[x * output_stride() + c]), uint32_t(qmax())) |
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
|
<< ", input offset = " << input_offset(); |
|
EXPECT_NEAR(float(int32_t(output[x * output_stride() + c])), output_real[x * channels() + c], 0.5f) |
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
|
<< ", input offset = " << input_offset() << ", accumulator = " << accumulator[x * channels() + c]; |
|
EXPECT_EQ(uint32_t(output_ref[x * channels() + c]), uint32_t(output[x * output_stride() + c])) |
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
|
<< ", input offset = " << input_offset() << ", accumulator = " << accumulator[x * channels() + c]; |
|
} |
|
} |
|
} |
|
} |
|
|
|
void Test( |
|
xnn_qu8_avgpool_minmax_multipass_ukernel_fn avgpool_minmax, |
|
xnn_init_qu8_avgpool_minmax_params_fn init_params, |
|
xnn_qu8_requantize_fn requantize) const |
|
{ |
|
std::random_device random_device; |
|
auto rng = std::mt19937(random_device()); |
|
std::uniform_int_distribution<int32_t> u8dist( |
|
std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max()); |
|
|
|
std::vector<const uint8_t*> indirect_input((output_pixels() - 1) * step() + packed_pooling_elements()); |
|
std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + |
|
input_offset() + indirect_input.size() * channels()); |
|
std::vector<uint8_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(uint8_t)); |
|
std::vector<uint8_t> output((output_pixels() - 1) * output_stride() + channels()); |
|
std::vector<uint8_t> output_ref(output_pixels() * channels()); |
|
std::vector<float> output_real(output_pixels() * channels()); |
|
std::vector<int32_t> accumulator(output_pixels() * channels()); |
|
std::vector<int32_t, AlignedAllocator<int32_t, 64>> buffer(XNN_EXTRA_BYTES / sizeof(uint8_t) + channels()); |
|
for (size_t iteration = 0; iteration < iterations(); iteration++) { |
|
do { |
|
std::generate(input.begin(), input.end(), [&]() { return u8dist(rng); }); |
|
} while (input.size() > 1 && *std::max_element(input.cbegin(), input.cend()) == *std::min_element(input.cbegin(), input.cend())); |
|
std::fill(input.begin(), input.begin() + input_offset(), UINT8_C(0xA5)); |
|
std::fill(input.end() - XNN_EXTRA_BYTES / sizeof(uint8_t), input.end(), UINT8_C(0xA5)); |
|
std::fill(output.begin(), output.end(), UINT8_C(0xA5)); |
|
|
|
for (size_t i = 0; i < (output_pixels() - 1) * step() + pooling_elements(); i++) { |
|
indirect_input[i] = input.data() + i * channels(); |
|
} |
|
std::shuffle(indirect_input.begin(), |
|
indirect_input.begin() + (output_pixels() - 1) * step() + pooling_elements(), rng); |
|
if (zero_index() != SIZE_MAX) { |
|
indirect_input[zero_index()] = zero.data(); |
|
} |
|
|
|
|
|
xnn_qu8_avgpool_minmax_params params; |
|
init_params( |
|
¶ms, |
|
-int32_t(input_zero_point()) * int32_t(pooling_elements()), |
|
input_scale() / (output_scale() * float(pooling_elements())), |
|
output_zero_point(), qmin(), qmax()); |
|
|
|
|
|
for (size_t x = 0; x < output_pixels(); x++) { |
|
for (size_t c = 0; c < channels(); c++) { |
|
int32_t acc = 0; |
|
for (size_t p = 0; p < pooling_elements(); p++) { |
|
const uint8_t* row = indirect_input[x * step() + p]; |
|
if (row != zero.data()) { |
|
acc += int32_t(row[c + input_offset()]); |
|
} |
|
acc -= int32_t(input_zero_point()); |
|
} |
|
accumulator[x * channels() + c] = acc; |
|
output_ref[x * channels() + c] = requantize( |
|
acc, input_scale() / (output_scale() * float(pooling_elements())), output_zero_point(), qmin(), qmax()); |
|
const float scaled_acc = |
|
float(acc) * input_scale() / (output_scale() * float(pooling_elements())) + float(output_zero_point()); |
|
output_real[x * channels() + c] = std::min(std::max(scaled_acc, float(qmin())), float(qmax())); |
|
} |
|
} |
|
|
|
|
|
avgpool_minmax(output_pixels(), pooling_elements(), channels(), |
|
indirect_input.data(), input_offset() * sizeof(uint8_t), zero.data(), |
|
buffer.data(), output.data(), |
|
(step() - (packed_pooling_elements() - incremental_pooling_tile())) * sizeof(void*), |
|
(output_stride() - channels()) * sizeof(uint8_t), |
|
¶ms); |
|
|
|
|
|
for (size_t x = 0; x < output_pixels(); x++) { |
|
for (size_t c = 0; c < channels(); c++) { |
|
EXPECT_GE(uint32_t(output[x * output_stride() + c]), uint32_t(qmin())) |
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
|
<< ", input offset = " << input_offset(); |
|
EXPECT_LE(uint32_t(output[x * output_stride() + c]), uint32_t(qmax())) |
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
|
<< ", input offset = " << input_offset(); |
|
EXPECT_NEAR(float(int32_t(output[x * output_stride() + c])), output_real[x * channels() + c], 0.5f) |
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
|
<< ", input offset = " << input_offset() << ", accumulator = " << accumulator[x * channels() + c]; |
|
EXPECT_EQ(uint32_t(output_ref[x * channels() + c]), uint32_t(output[x * output_stride() + c])) |
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
|
<< ", input offset = " << input_offset() << ", accumulator = " << accumulator[x * channels() + c]; |
|
} |
|
} |
|
} |
|
} |
|
|
|
void Test(xnn_f16_pavgpool_minmax_unipass_ukernel_fn pavgpool_minmax, xnn_init_f16_minmax_params_fn init_params) const { |
|
std::random_device random_device; |
|
auto rng = std::mt19937(random_device()); |
|
std::uniform_real_distribution<float> f32dist; |
|
std::uniform_real_distribution<float> m32dist(0.1f, 0.5f); |
|
|
|
std::vector<const uint16_t*> indirect_input((output_pixels() - 1) * step() + packed_pooling_elements()); |
|
std::vector<uint16_t> input(XNN_EXTRA_BYTES / sizeof(uint16_t) + |
|
input_offset() + indirect_input.size() * channels()); |
|
std::vector<uint16_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(uint16_t)); |
|
std::vector<uint16_t> multiplier(output_pixels()); |
|
std::vector<uint16_t> output((output_pixels() - 1) * output_stride() + channels()); |
|
std::vector<float> output_ref(output_pixels() * channels()); |
|
for (size_t iteration = 0; iteration < iterations(); iteration++) { |
|
std::generate(input.begin(), input.end(), [&]() { return fp16_ieee_from_fp32_value(f32dist(rng)); }); |
|
std::fill(input.begin(), input.begin() + input_offset(), UINT16_C(0x7E00) ); |
|
std::fill(input.end() - XNN_EXTRA_BYTES / sizeof(uint16_t), input.end(), UINT16_C(0x7E00) ); |
|
std::generate(multiplier.begin(), multiplier.end(), [&]() { return fp16_ieee_from_fp32_value(m32dist(rng)); }); |
|
std::fill(output.begin(), output.end(), UINT16_C(0x7E00) ); |
|
|
|
for (size_t i = 0; i < (output_pixels() - 1) * step() + pooling_elements(); i++) { |
|
indirect_input[i] = input.data() + i * channels(); |
|
} |
|
std::shuffle(indirect_input.begin(), |
|
indirect_input.begin() + (output_pixels() - 1) * step() + pooling_elements(), rng); |
|
if (zero_index() != SIZE_MAX) { |
|
indirect_input[zero_index()] = zero.data(); |
|
} |
|
|
|
|
|
for (size_t x = 0; x < output_pixels(); x++) { |
|
for (size_t c = 0; c < channels(); c++) { |
|
float acc = 0.0f; |
|
for (size_t p = 0; p < pooling_elements(); p++) { |
|
const uint16_t* row = indirect_input[x * step() + p]; |
|
if (row != zero.data()) { |
|
acc += fp16_ieee_to_fp32_value(row[c + input_offset()]); |
|
} |
|
} |
|
output_ref[x * channels() + c] = acc * fp16_ieee_to_fp32_value(multiplier[x]); |
|
} |
|
} |
|
|
|
|
|
const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend()); |
|
const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend()); |
|
const float accumulated_range = accumulated_max - accumulated_min; |
|
float output_min_as_float = accumulated_min + float(qmin()) / 255.0f * accumulated_range; |
|
float output_max_as_float = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range; |
|
const uint16_t output_min_as_half = fp16_ieee_from_fp32_value(output_min_as_float); |
|
const uint16_t output_max_as_half = fp16_ieee_from_fp32_value(output_max_as_float); |
|
output_min_as_float = fp16_ieee_to_fp32_value(output_min_as_half); |
|
output_max_as_float = fp16_ieee_to_fp32_value(output_max_as_half); |
|
|
|
|
|
for (float& output_value : output_ref) { |
|
output_value = std::max(std::min(output_value, output_max_as_float), output_min_as_float); |
|
} |
|
|
|
|
|
xnn_f16_minmax_params params; |
|
init_params(¶ms, output_min_as_half, output_max_as_half); |
|
|
|
|
|
pavgpool_minmax(output_pixels(), pooling_elements(), channels(), |
|
reinterpret_cast<const void**>(indirect_input.data()), input_offset() * sizeof(uint16_t), zero.data(), |
|
multiplier.data(), output.data(), |
|
step() * sizeof(void*), |
|
(output_stride() - channels()) * sizeof(uint16_t), |
|
¶ms); |
|
|
|
|
|
for (size_t x = 0; x < output_pixels(); x++) { |
|
for (size_t c = 0; c < channels(); c++) { |
|
EXPECT_GE(fp16_ieee_to_fp32_value(output[x * output_stride() + c]), output_min_as_float) |
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
|
<< ", input offset = " << input_offset(); |
|
EXPECT_LE(fp16_ieee_to_fp32_value(output[x * output_stride() + c]), output_max_as_float) |
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
|
<< ", input offset = " << input_offset(); |
|
EXPECT_NEAR( |
|
fp16_ieee_to_fp32_value(output[x * output_stride() + c]), |
|
output_ref[x * channels() + c], |
|
std::max(1.0e-4f, std::abs(output_ref[x * channels() + c]) * 3.0e-3f)) |
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
|
<< ", input offset = " << input_offset(); |
|
} |
|
} |
|
} |
|
} |
|
|
|
void Test(xnn_f16_pavgpool_minmax_multipass_ukernel_fn pavgpool_minmax, xnn_init_f16_minmax_params_fn init_params) const { |
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std::random_device random_device; |
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auto rng = std::mt19937(random_device()); |
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std::uniform_real_distribution<float> f32dist; |
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std::uniform_real_distribution<float> m32dist(0.1f, 0.5f); |
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std::vector<const uint16_t*> indirect_input((output_pixels() - 1) * step() + packed_pooling_elements()); |
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std::vector<uint16_t> input(XNN_EXTRA_BYTES / sizeof(uint16_t) + |
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input_offset() + indirect_input.size() * channels()); |
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std::vector<uint16_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(uint16_t)); |
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std::vector<uint16_t> multiplier(output_pixels()); |
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std::vector<uint16_t> output((output_pixels() - 1) * output_stride() + channels()); |
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std::vector<float> output_ref(output_pixels() * channels()); |
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std::vector<uint16_t, AlignedAllocator<uint16_t, 64>> buffer(XNN_EXTRA_BYTES / sizeof(uint16_t) + channels()); |
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for (size_t iteration = 0; iteration < iterations(); iteration++) { |
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std::generate(input.begin(), input.end(), [&]() { return fp16_ieee_from_fp32_value(f32dist(rng)); }); |
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std::fill(input.begin(), input.begin() + input_offset(), UINT16_C(0x7E00) ); |
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std::fill(input.end() - XNN_EXTRA_BYTES / sizeof(uint16_t), input.end(), UINT16_C(0x7E00) ); |
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std::generate(multiplier.begin(), multiplier.end(), [&]() { return fp16_ieee_from_fp32_value(m32dist(rng)); }); |
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std::fill(output.begin(), output.end(), UINT16_C(0x7E00) ); |
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for (size_t i = 0; i < (output_pixels() - 1) * step() + pooling_elements(); i++) { |
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indirect_input[i] = input.data() + i * channels(); |
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} |
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std::shuffle(indirect_input.begin(), |
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indirect_input.begin() + (output_pixels() - 1) * step() + pooling_elements(), rng); |
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if (zero_index() != SIZE_MAX) { |
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indirect_input[zero_index()] = zero.data(); |
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} |
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for (size_t x = 0; x < output_pixels(); x++) { |
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for (size_t c = 0; c < channels(); c++) { |
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float acc = 0.0f; |
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for (size_t p = 0; p < pooling_elements(); p++) { |
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const uint16_t* row = indirect_input[x * step() + p]; |
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if (row != zero.data()) { |
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acc += fp16_ieee_to_fp32_value(row[c + input_offset()]); |
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} |
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} |
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output_ref[x * channels() + c] = acc * fp16_ieee_to_fp32_value(multiplier[x]); |
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} |
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} |
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const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend()); |
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const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend()); |
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const float accumulated_range = accumulated_max - accumulated_min; |
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float output_min_as_float = accumulated_min + float(qmin()) / 255.0f * accumulated_range; |
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float output_max_as_float = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range; |
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const uint16_t output_min_as_half = fp16_ieee_from_fp32_value(output_min_as_float); |
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const uint16_t output_max_as_half = fp16_ieee_from_fp32_value(output_max_as_float); |
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output_min_as_float = fp16_ieee_to_fp32_value(output_min_as_half); |
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output_max_as_float = fp16_ieee_to_fp32_value(output_max_as_half); |
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for (float& output_value : output_ref) { |
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output_value = std::max(std::min(output_value, output_max_as_float), output_min_as_float); |
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} |
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xnn_f16_minmax_params params; |
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init_params(¶ms, output_min_as_half, output_max_as_half); |
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pavgpool_minmax(output_pixels(), pooling_elements(), channels(), |
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reinterpret_cast<const void**>(indirect_input.data()), input_offset() * sizeof(uint16_t), zero.data(), |
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multiplier.data(), buffer.data(), output.data(), |
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(step() - (packed_pooling_elements() - incremental_pooling_tile())) * sizeof(void*), |
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(output_stride() - channels()) * sizeof(uint16_t), |
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¶ms); |
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for (size_t x = 0; x < output_pixels(); x++) { |
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for (size_t c = 0; c < channels(); c++) { |
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EXPECT_GE(fp16_ieee_to_fp32_value(output[x * output_stride() + c]), output_min_as_float) |
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
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<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
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<< ", input offset = " << input_offset(); |
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EXPECT_LE(fp16_ieee_to_fp32_value(output[x * output_stride() + c]), output_max_as_float) |
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
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<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
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<< ", input offset = " << input_offset(); |
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EXPECT_NEAR( |
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fp16_ieee_to_fp32_value(output[x * output_stride() + c]), |
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output_ref[x * channels() + c], |
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std::max(1.0e-4f, std::abs(output_ref[x * channels() + c]) * 3.0e-3f)) |
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
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<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
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<< ", input offset = " << input_offset(); |
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} |
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} |
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} |
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} |
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void Test(xnn_f32_pavgpool_minmax_unipass_ukernel_fn pavgpool_minmax, xnn_init_f32_minmax_params_fn init_params) const { |
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std::random_device random_device; |
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auto rng = std::mt19937(random_device()); |
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std::uniform_real_distribution<float> f32dist; |
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std::uniform_real_distribution<float> m32dist(0.1f, 0.5f); |
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std::vector<const float*> indirect_input((output_pixels() - 1) * step() + packed_pooling_elements()); |
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std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + |
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input_offset() + indirect_input.size() * channels()); |
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std::vector<float> zero(channels() + XNN_EXTRA_BYTES / sizeof(float)); |
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std::vector<float> multiplier(output_pixels()); |
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std::vector<float> output((output_pixels() - 1) * output_stride() + channels()); |
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std::vector<float> output_ref(output_pixels() * channels()); |
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for (size_t iteration = 0; iteration < iterations(); iteration++) { |
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std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); }); |
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std::fill(input.begin(), input.begin() + input_offset(), std::nanf("")); |
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std::fill(input.end() - XNN_EXTRA_BYTES / sizeof(float), input.end(), std::nanf("")); |
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std::generate(multiplier.begin(), multiplier.end(), [&]() { return m32dist(rng); }); |
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std::fill(output.begin(), output.end(), std::nanf("")); |
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for (size_t i = 0; i < (output_pixels() - 1) * step() + pooling_elements(); i++) { |
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indirect_input[i] = input.data() + i * channels(); |
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} |
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std::shuffle(indirect_input.begin(), |
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indirect_input.begin() + (output_pixels() - 1) * step() + pooling_elements(), rng); |
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if (zero_index() != SIZE_MAX) { |
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indirect_input[zero_index()] = zero.data(); |
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} |
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for (size_t x = 0; x < output_pixels(); x++) { |
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for (size_t c = 0; c < channels(); c++) { |
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float acc = 0.0f; |
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for (size_t p = 0; p < pooling_elements(); p++) { |
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const float* row = indirect_input[x * step() + p]; |
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if (row != zero.data()) { |
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acc += row[c + input_offset()]; |
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} |
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} |
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output_ref[x * channels() + c] = acc * multiplier[x]; |
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} |
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} |
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const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend()); |
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const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend()); |
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const float accumulated_range = accumulated_max - accumulated_min; |
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const float output_min = accumulated_min + float(qmin()) / 255.0f * accumulated_range; |
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const float output_max = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range; |
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for (float& output_value : output_ref) { |
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output_value = std::max(std::min(output_value, output_max), output_min); |
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} |
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xnn_f32_minmax_params params; |
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init_params(¶ms, output_min, output_max); |
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pavgpool_minmax(output_pixels(), pooling_elements(), channels(), |
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indirect_input.data(), input_offset() * sizeof(float), zero.data(), |
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multiplier.data(), output.data(), |
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step() * sizeof(void*), |
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(output_stride() - channels()) * sizeof(float), |
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¶ms); |
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for (size_t x = 0; x < output_pixels(); x++) { |
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for (size_t c = 0; c < channels(); c++) { |
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EXPECT_GE(output[x * output_stride() + c], output_min) |
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
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<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
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<< ", input offset = " << input_offset(); |
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EXPECT_LE(output[x * output_stride() + c], output_max) |
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
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<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
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<< ", input offset = " << input_offset(); |
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EXPECT_NEAR( |
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output[x * output_stride() + c], |
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output_ref[x * channels() + c], |
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std::abs(output_ref[x * channels() + c]) * 1.0e-6f) |
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
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<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
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<< ", input offset = " << input_offset(); |
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} |
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} |
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} |
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} |
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void Test(xnn_f32_pavgpool_minmax_multipass_ukernel_fn pavgpool_minmax, xnn_init_f32_minmax_params_fn init_params) const { |
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std::random_device random_device; |
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auto rng = std::mt19937(random_device()); |
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std::uniform_real_distribution<float> f32dist; |
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std::uniform_real_distribution<float> m32dist(0.1f, 0.5f); |
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std::vector<const float*> indirect_input((output_pixels() - 1) * step() + packed_pooling_elements()); |
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std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + |
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input_offset() + indirect_input.size() * channels()); |
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std::vector<float> zero(channels() + XNN_EXTRA_BYTES / sizeof(float)); |
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std::vector<float> multiplier(output_pixels()); |
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std::vector<float> output((output_pixels() - 1) * output_stride() + channels()); |
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std::vector<float> output_ref(output_pixels() * channels()); |
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std::vector<float, AlignedAllocator<float, 64>> buffer(XNN_EXTRA_BYTES / sizeof(float) + channels()); |
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for (size_t iteration = 0; iteration < iterations(); iteration++) { |
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std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); }); |
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std::fill(input.begin(), input.begin() + input_offset(), std::nanf("")); |
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std::fill(input.end() - XNN_EXTRA_BYTES / sizeof(float), input.end(), std::nanf("")); |
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std::generate(multiplier.begin(), multiplier.end(), [&]() { return m32dist(rng); }); |
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std::fill(output.begin(), output.end(), std::nanf("")); |
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for (size_t i = 0; i < (output_pixels() - 1) * step() + pooling_elements(); i++) { |
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indirect_input[i] = input.data() + i * channels(); |
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} |
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std::shuffle(indirect_input.begin(), |
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indirect_input.begin() + (output_pixels() - 1) * step() + pooling_elements(), rng); |
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if (zero_index() != SIZE_MAX) { |
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indirect_input[zero_index()] = zero.data(); |
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} |
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for (size_t x = 0; x < output_pixels(); x++) { |
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for (size_t c = 0; c < channels(); c++) { |
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float acc = 0.0f; |
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for (size_t p = 0; p < pooling_elements(); p++) { |
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const float* row = indirect_input[x * step() + p]; |
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if (row != zero.data()) { |
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acc += row[c + input_offset()]; |
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} |
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} |
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output_ref[x * channels() + c] = acc * multiplier[x]; |
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} |
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} |
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const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend()); |
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const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend()); |
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const float accumulated_range = accumulated_max - accumulated_min; |
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const float output_min = accumulated_min + float(qmin()) / 255.0f * accumulated_range; |
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const float output_max = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range; |
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for (float& output_value : output_ref) { |
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output_value = std::max(std::min(output_value, output_max), output_min); |
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} |
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xnn_f32_minmax_params params; |
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init_params(¶ms, output_min, output_max); |
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pavgpool_minmax(output_pixels(), pooling_elements(), channels(), |
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indirect_input.data(), input_offset() * sizeof(float), zero.data(), |
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multiplier.data(), buffer.data(), output.data(), |
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(step() - (packed_pooling_elements() - incremental_pooling_tile())) * sizeof(void*), |
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(output_stride() - channels()) * sizeof(float), |
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¶ms); |
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for (size_t x = 0; x < output_pixels(); x++) { |
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for (size_t c = 0; c < channels(); c++) { |
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EXPECT_GE(output[x * output_stride() + c], output_min) |
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
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<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
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<< ", input offset = " << input_offset(); |
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EXPECT_LE(output[x * output_stride() + c], output_max) |
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
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<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
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<< ", input offset = " << input_offset(); |
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EXPECT_NEAR( |
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output[x * output_stride() + c], |
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output_ref[x * channels() + c], |
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std::abs(output_ref[x * channels() + c]) * 1.0e-6f) |
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels() |
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<< ", pooling elements = " << pooling_elements() << ", step = " << step() |
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<< ", input offset = " << input_offset(); |
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} |
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} |
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} |
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} |
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private: |
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size_t output_pixels_{1}; |
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size_t pooling_elements_{1}; |
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size_t channels_{1}; |
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size_t input_offset_{0}; |
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size_t zero_index_{SIZE_MAX}; |
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size_t step_{1}; |
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size_t primary_pooling_tile_{1}; |
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size_t incremental_pooling_tile_{1}; |
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size_t output_stride_{0}; |
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float input_scale_{1.25f}; |
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float output_scale_{0.75f}; |
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uint8_t input_zero_point_{121}; |
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uint8_t output_zero_point_{133}; |
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uint8_t qmin_{0}; |
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uint8_t qmax_{255}; |
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size_t iterations_{3}; |
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}; |
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